Agent skill · research science · aiskillstore
agentdb-reinforcement-learning-training
Train AI agents using AgentDB's 9 reinforcement learning algorithms including Q-Learning, DQN, PPO, and Actor-Critic. Build self-learning agents, implement RL training loops with experience replay, and deploy optimized models to production.
Why this skill is useful
Adds executable scripts for training RL agents using specific algorithms and deployment pipelines not commonly found in public resources.
What it needs
Requires agentdb-learning installed locally. About 8k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
AgentDB Reinforcement Learning Training Overview Train AI learning plugins with AgentDB's 9 reinforcement learning algorithms including Decision Transformer, Q-Learning, SARSA, Actor-Critic, PPO, and more. Build self-learning agents, implement RL, and optimize agent behavior through experience. When to Use This Skill Use this skill when you need to: Train autonomous agents that learn from experience Implement reinforcement learning systems Optimize agent behavior through trial and error Build self-improving AI systems Deploy RL agents in production environments Benchmark and compare RL algorithms Available RL Algorithms 1. Q-Learning - Value-based, off-policy 2. SARSA - Value-based, on-policy 3. Deep Q-Network (DQN) - Deep RL with experience replay 4. Actor-Critic - Policy gradient with value baseline 5. Proximal Policy Optimization (PPO) - Trust region policy optimization 6. Decision Transformer - Offline RL with transformers 7. Advantage Actor-Critic (A2C) - Synchronous advantage estimation 8. Twin Delayed DDPG (TD3) - Continuous control 9. Soft Actor-Critic (SAC) - Maximum entropy RL SOP Framework: 5-Phase RL Training Deployment Phase 1: Initialize Learning Environment (1-2 hours) Objective: Setup AgentDB learning infrastructure with environment configuration Agent: ml-developer Steps: 1. Install AgentDB Learning Module 2. Initialize learning database 3. Define environment 4. Setup monitoring Memory Pattern: Validation: Learning database initialized Environment configured and tested Monitor capturing metrics Configuration stored in memory Phase 2: Configure RL Algorithm (1-2 hours) Objective: Select and configure RL algorithm for the learning task Agent: ml-developer Steps: 1. Select algorithm 2. Configure hyperparameters 3. Setup experience replay 4. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/agentdb-reinforcement-learning-training